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The aim of the study is to present the implications for the financing and sustainability of enterprises based on a ranking methodology for categorical financial data.

Taking advantage of the optimal scaling properties of correspondence analysis (CA), a ranking‐clustering procedure is proposed. The proposed method was applied to categorical financial variables (i.e family farm income, gross profit, gross income, labour income and profitability) collected from a stratified random sampling of 80 Greek pig farms using a structured questionnaire.

The cluster analysis revealed three distinct groups of pig farms. Several recommendations for managerial practices and financial development resulted from this study. For the farms belonging to cluster C₁, that present low rankings on both criteria, a development planning process must be applied that will focus on organizational and management issues. For the farms belonging to cluster C₂, that present low rankings on the “composite income” criterion, policy measures have to be undertaken, aiming at exploiting their own production coefficients, reducing fixed costs and increasing productivity. Finally, for the farms in cluster C₃, that present high scores on both ranking criteria, it is recommended to take actions that will improve their competitiveness.

The findings are limited to five selected financial variables. Therefore, future studies in the same or other business fields would benefit from incorporating a greater number of variables.

The proposed methodological scheme could be useful to practitioners and academics, due to the fact that limited studies have dealt with this ranking problem, particularly in relation to the Greek agricultural business environment.

This study delves into the determinants and praxis of derivative hedging instruments (DHIs) usage of Malta, a small island state. Empirical evidence is also provided in relation to the impact of DHI usage and the adoption of a hedge accounting (HA) model in entities’ financial statements. A mixed methodology design is deployed involving: (1) a series of statistical models and tests and (2) seven semi-structured interviews with senior professionals.

The data collected comprise proxy variable values collected from the financial statements of 568 firm-years from 107 Maltese entities between the years 2009 and 2014. Greater likelihood of financial distress, decreasing investment efficiency and increased levels of gearing, are identified as being significant determinants for the use of DHIs. Although DHI usage is low in comparison to larger states, it has been increasing over the period under study.

HA is evidenced to be less popular in Malta, but the study evidences correlation between certain DHIs and HA usage. The quantitative statistical model results in evidence with no significant earnings volatility (EV) or cash flow volatility (CFV) reduction effects through the application of HA. Albeit, the study finds a significant CFV reduction effect emanating from DHI usage, but no corresponding EV reduction effect.

Better education and dissemination of the HA treatment by auditors and regulatory bodies could help propagate the HA treatment, potentially enhancing the EV reduction effectiveness of DHI use. This research provides empirical evidence to substantiate the rationale behind utilising DHIs in smaller island states, especially when coupled with a sound risk management culture.

The aim of the study is to propose certain agricultural policy measures for the financing and development of Greek farms, established by young farmers, based on the results of a clustering method suitable for handling socio‐economic categorical data.

The clustering method was applied to categorical data collected from 110 randomly selected investment plans of Greek agricultural farms. The investment plans were submitted to the “Region of Central Macedonia” administrative office, in the framework of the Operational Programme “Agricultural Development – Reform of the Countryside 2000‐2006” and refer to agricultural investments by “Young Farmers”, according to the terms and conditions of Priority Axis III: “Improvement of the Age Composition of the Agricultural Population”. The input variables for the analyses were the farmers' gender, age class, education level and permanent place of residence, the farms' agricultural activity, Human Labour Units (HLU) and farms' viability level. All these variables were measured on nominal or ordinal scales. The available data were analyzed by means of a hierarchical cluster analysis method applied on the rows of an appropriate matrix of a complete disjunctive form with a dummy coding 0 or 1. The similarities were measured through the Benzécri'sχ²distance (metric), while the Ward's method was used as a criterion for cluster formation.

Five clusters of farms emerged, with statistically significant diverse socio‐economic profiles. The most important impact on the formation of the groups of farms was found to be related to the number of HLU, the farmers' level of education and gender. This derived typology allows for the determination of a flexible development and funding policy for the agricultural farms, based on the socio‐economic profile of the formulated clusters.

One of the limitations of the current study derives from the fact that the clustering method used is suitable only for categorical, non‐metric data. Another limitation comes from the fact that a relative small number of investment plans were used in the analysis. A larger sample covering and other geographical regions is needed in order to confirm the current results and make nation‐wide comparisons and “tailor‐made” proposals for financing and development. Finally, it is interesting to contact longitudinal surveys in order to evaluate the effectiveness of the funding policy of the corresponding programme.

The study's results could be useful to practitioners and academics because certain agricultural policy measures for the financing and development of Greek farms established by young farmers are proposed. Additionally, the data analysis method used in this study offers an alternative way for clustering categorical data.

There is a lack of a framework that explicates how to determine the benefits that consumers desire from a product. The purpose of this article is to formulate a scientific procedure for discerning the benefits that consumers seek from a product. The authors term this procedure as visual thematic analysis (VTA). VTA procedure is illustrated through discerning the benefits of mainstream (non‐financial) English newspapers.

The focus group method was used to collect data. These data were analyzed using visual thematic analysis which involves using multiple investigators and multi‐dimensional scaling techniques in stages.

A total of 26 newspaper attributes combined to form eight distinct newspaper benefits namely ease of comprehension, journalistic values, critical insights, general news, entertainment, well‐being, classifieds and offers.

Obtained results may be used further: to segment the newspaper market based upon benefits sought, to position newspapers within the desired segment(s) and to fashion product mix in a way that appeals to the targeted segment(s).

This paper proposes a new method called “visual thematic analysis” for data reduction. One such application of VTA is “discerning product benefits” which is discussed in detail. Other applications of this technique that are mentioned in the paper are in the areas of data reduction when researcher confronts small sample size, data reduction of categorical variables and scale development.

Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.

Companies in all industries are searching for new sources of competitive advantage since the competition in their marketplace is becoming increasingly intensive. The resource-based view of the firm explains the sources of sustainable competitive advantages. From a resource-based view perspective, relational based assets (i.e., the assets resulting from firm contacts in the marketplace) enable competitive advantage. The relational based assets examined in this work are brand image and corporate reputation, as components of brand equity, and customer value. This paper explores how they create value. Despite the relatively large amount of literature describing the benefits of firms in having strong brand equity and delivering customer value, no research validated the linkage of brand equity components, brand image, and corporate reputation, simultaneously in the customer value–customer loyalty chain. This work presents a model of testing these relationships in consumer goods, in a business-to-business context. The results demonstrate the differential roles of brand image and corporate reputation on perceived quality, customer value, and customer loyalty. Brand image influences the perception of quality of the products and the additional services, whereas corporate reputation actions beyond brand image, estimating the customer value and customer loyalty. The effects of corporate reputation are also validated on different samples. The results demonstrate the importance of managing brand equity facets, brand image, and corporate reputation since their differential impacts on perceived quality, customer value, and customer loyalty. The results also demonstrate that companies should not limit to invest only in brand image. Maintaining and enhancing corporate reputation can have a stronger impact on customer value and customer loyalty, and can create differential competitive advantage.

Means, medians and SD for available socio‐economic status (SES) black‐white differences are here substituted for those of IQ in a between‐groups model published by the author over a decade ago. The goodness of fit of the SES variables used is compared with that for the earlier IQ data. Even when SES variables are relatively successful this can be viewed as additional evidence of the importance of IQ differences to black‐white differences in delinquency.